You can speed up training on a single- or multiple-GPU workstation (with Parallel Computing Toolbox™), or scale up to clusters and clouds, including NVIDIA ® GPU Cloud and Amazon EC2 ® GPU instances (with MATLAB Parallel Server™). The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models. You can also export Deep Learning Toolbox networks and layer graphs to TensorFlow 2 and the ONNX model format.
You can analyze S-parameters convert among S, Y, Z, T, and other network parameters and visualize RF data using rectangular and polar plots and Smith ® Charts. You can import networks and layer graphs from TensorFlow™ 2, TensorFlow-Keras, PyTorch ®, the ONNX™ (Open Neural Network Exchange) model format, and Caffe. The toolbox provides functions for analyzing, manipulating, and visualizing RF data. You can visualize layer activations and graphically monitor training progress.
#MATLAB RF TOOLBOX PDF CODE#
The Experiment Manager app helps you manage multiple deep learning experiments, keep track of training parameters, analyze results, and compare code from different experiments. With the Deep Network Designer app, you can design, analyze, and train networks graphically. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data.
Deep Learning Toolbox™ provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps.
0 kommentar(er)
